Apparatuses, systems and methods for acquiring images of occupants inside a vehicle

ABSTRACT

The present disclosure is directed to apparatuses, systems and methods for acquiring images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for acquiring images of occupants inside a vehicle using a vehicle in-cabin device that automatically determines a location of the device inside the vehicle.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 14/994,299, entitled APPARATUSES, SYSTEMS AND METHODS FORACQUIRING IMAGES OF OCCUPANTS INSIDE A VEHICLE, filed Jan. 13, 2016, thedisclosure of which is incorporated herein in its entirety by reference.

TECHNICAL FIELD

The present disclosure is directed to apparatuses, systems and methodsfor acquiring images of occupants inside a vehicle. More particularly,the present disclosure is directed to apparatuses, systems and methodsfor acquiring images of occupants inside a vehicle using a vehiclein-cabin device that automatically determines a location of the deviceinside the vehicle.

BACKGROUND

Vehicles are being provided with more complex systems. For example,vehicles commonly include a plethora of entertainment systems, such asstereos, USB interfaces for mobile telephones, video players, etc.Vehicles often have a host of other operator interfaces, such asemergency calling systems, vehicle navigation systems, heating and airconditioning systems, interior and exterior lighting controls, air bags,seatbelts, etc.

Vehicle operating environments are becoming more complex as well. Forexample, some roadways include u-turn lanes, round-a-bouts, no-leftturn, multiple lanes one way in the morning and the other way in theafternoon, etc. Increases in traffic are also contributing to increasedcomplexity.

These additional complexities contribute to increases in driverdistractions. A great deal of innovation is taking place related tovehicle in-cabin devices for identifying driver distractions, and forreducing driver distractions.

What is needed are apparatuses, systems, and methods for automaticallydetermining a vehicle in-cabin device location. What is further neededare methods and systems for generating data representative of vehiclein-cabin insurance risk evaluations based on data representative ofskeletal diagrams of a driver that are indicative of driverdistractions.

SUMMARY

A vehicle in-cabin device for generating data representative of at leastone skeletal diagram of at least one occupant within an associatedvehicle may include a processor and a memory. The vehicle in-cabindevice may further include at least one sensor for generating vehicleinterior data, wherein the vehicle interior data is representative of alocation of the vehicle in-cabin device within the vehicle. The vehiclein-cabin device may also include a vehicle in-cabin device locationdetermination module stored on the memory that, when executed by theprocessor, causes the processor to generate vehicle in-cabin devicelocation data based on the vehicle interior data, wherein the vehiclein-cabin device location data is representative of a location andorientation of the vehicle in-cabin device within the vehicle interior.

In another embodiment, a computer-implemented method for automaticallyregistering a location of a vehicle in-cabin device within an interiorof a vehicle may include positioning the vehicle in-cabin device withinthe interior of the vehicle. The method receiving, at a processor of acomputing device, vehicle in-cabin device registration location datafrom a sensor of the vehicle in-cabin device, wherein the vehiclein-cabin device registration location data is representative of at leastone of: a two-dimensional location of the vehicle in-cabin device withinthe vehicle or a three-dimensional location of the vehicle in-cabindevice within the vehicle and at least one of: a vehicle in-cabin devicetilt value, a vehicle in-cabin device pan value, or a vehicle in-cabindevice roll value. The method may also storing the vehicle in-cabindevice registration location data within a memory.

In a further embodiment, a non-transitory computer-readable mediumstoring computer-readable instruction that, when executed by aprocessor, may cause the processor to automatically register a locationof a vehicle in-cabin device within an interior of a vehicle. Thenon-transitory computer-readable medium may also include a vehiclein-cabin device registration location data receiving module that, whenexecuted by the processor, causes the processor to receive vehiclein-cabin device registration location data from a sensor of the vehiclein-cabin device, wherein the vehicle in-cabin device registrationlocation data is representative of at least one of: a two-dimensionallocation of the vehicle in-cabin device within the vehicle or athree-dimensional location of the vehicle in-cabin device within thevehicle and at least one of: a vehicle in-cabin device tilt value, avehicle in-cabin device pan value, or a vehicle in-cabin device rollvalue. The non-transitory computer-readable medium may further includestoring the vehicle in-cabin device registration location data within amemory.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A-1C depict various views of the interior of an example vehiclethat illustrate locations of vehicle operator monitoring devices withinthe vehicle;

FIGS. 2A-2C illustrate various example images constructed from dataretrieved from the vehicle monitoring devices of FIGS. 1A-1C;

FIG. 3 illustrates a block diagram of a computer network, a computerserver and an on-board vehicle computer on which an exemplary vehiclein-cabin device location may be automatically determined within avehicle interior may operate in accordance with the describedembodiments;

FIG. 4 illustrates a block diagram of an exemplary vehicle in-cabindevice for use in automatically determining a location of the vehiclein-cabin device within a vehicle interior;

FIG. 5 depicts a flow diagram of an example method of automaticallydetermining a vehicle in-cabin device location within a vehicleinterior; and

FIG. 6 depicts a flow diagram of an example method of automaticallydetermining a vehicle in-cabin device location within a vehicleinterior.

DETAIL DESCRIPTION

Apparatuses, systems and methods for acquiring images of occupantsinside a vehicle may include using a vehicle in-cabin device thatautomatically determines a location of the device inside the vehicle.For example, a vehicle in-cabin device may be provided to an insuredindividual, and the insured individual may place the vehicle in-cabindevice within an interior of an associated vehicle. As described herein,the vehicle in-cabin device may automatically determine an associatedlocation and/or orientation of the vehicle in-cabin device. Thereby, aninsured individual may select a location for the vehicle in-cabindevice, and may not require assistance from an installer. Thus, anassociated insurance provider may provide a vehicle in-cabin devicedirectly to an insured individual without enlisting any third-party.

A vehicle in-cabin device may include features (e.g., a processor, amemory and sensors) that are configured to automatically determine anassociated location and/or orientation. For example, a memory mayinclude computer-readable instructions stored thereon that, whenexecuted by a processor, cause the processor to automatically receiveinputs from various sensors, and determine a location and/or orientationof the vehicle in-cabin device free of any calibration by, for example,an insured individual or an installation technician. Thereby, vehiclein-cabin device installation time and cost are reduced relative to, forexample, a device that requires an installation technician and/or usercalibration. Accordingly, associated memory, processing, and relateddata transmission requirements are reduced compared to previousapproaches.

Based on the determined location and/or orientation, a processor,executing an adjustment algorithm, may adjust associated image such thatreal-time image data may be correlated with previously classified imagedata. As a particular example, a vehicle in-cabin device may be locatedand oriented such that an associated sensor is 2 cm towards a driverfrom a passenger-side A-pillar, 5 cm above a dashboard, and upside down(i.e., 180 degree rotation about the Z-axis). Thereby, the apparatuses,systems and methods of the present disclosure may adjust current imagedata right-side up and may compare the adjusted image data to previouslyclassified image data.

Related methods and systems for generating data representative ofvehicle in-cabin insurance risk evaluations may include, for example,the following capabilities: 1) determine whether a vehicle driver islooking at a road (i.e., tracking the driver's face/eyes, with emphasison differentiating between similar actions, such as a driver who isadjusting a radio while looking at the road versus adjusting the radiowhile not looking at the road at all); 2) determine whether a driver'shands are empty (e.g., including determining an approximate size/shapeof an object in a driver's hands to, for example, differentiate betweena cell phone and a large cup, for example); 3) identify a finite numberof vehicle occupant postures; and 4) vehicle occupant postures, that arelogged, may be rotated and scaled to be normalized for a range ofdifferent drivers.

An associated mobile application may accommodate all computer systemplatforms, such as, iOS, Android and Windows, to connect a vehiclein-cabin device to, for example, a cell phone. In addition, to act asdata connection provider to remote servers, the mobile application mayprovide a user friendly interface for reporting and troubleshootingvehicle in-cabin device operation.

Turning to FIGS. 1A-1C, vehicle interior monitoring systems 100 a, 100b, 100 c are illustrated. As depicted in FIG. 1A, the vehicle interiormonitoring system 100 a may include a center-dash vehicle in-cabindevice position 125 a located in a center area of a dash, a driver-sideA-pillar vehicle in-cabin device position 135 a located in a driver sideA-pillar 130 a, a passenger-side A-pillar vehicle in-cabin deviceposition 145 a located in a passenger-side A-pillar 140 a and a rearviewmirror vehicle in-cabin device position 160 a located on a bottom-sideof the rearview mirror 155 a. The vehicle interior monitoring system 100a may further, or alternatively, include vehicle in-cabin devicepositions in a driver-side visor 165 a, a passenger-side visor 170 a, arearview mirror mounting bracket 150 a and, or the steering wheel 110 a.As described in detail herein, a position of a left-hand 115 a of avehicle driver and, or a position of a right-hand 120 a of the vehicledriver, relative to, for example, a vehicle steering wheel 110 a may bedetermined based on data acquired from any one of the vehicle in-cabindevice positions 125 a, 135 a, 145 a, 160 a. Any one of the vehiclein-cabin device positions 125 a, 135 a, 145 a, 160 a may beautomatically determined based on, for example, an input from an imagesensor, an infrared sensor, an ultrasonic sensor, a compass sensor, aGPS sensor, a microphone or any other suitable sensor.

With reference to FIG. 1B, the vehicle monitoring system 100 b mayinclude a driver-side B-pillar vehicle in-cabin device position 180 blocated in a driver-side B-pillar 175 b and a center-dash vehiclein-cabin device position 125 b located in a center area of the dash.While not shown in FIG. 1B, the vehicle monitoring system 100 b mayinclude a passenger-side B-pillar vehicle in-cabin device position and,or any other vehicle in-cabin device position as described inconjunction with FIG. 1A. The vehicle monitoring system 100 b mayfurther include a display device 185 b. The display device 185 b may belocated in, for example, a vehicle in-cabin device located in acenter-console area. As illustrated in FIG. 1B, data acquired from thevehicle in-cabin device 125 b, 180 b may be used to automaticallydetermine a location of the vehicle in-cabin device, a position of adriver-side seat 190 b, a passenger-side seat 195 b, a steering wheel110 b and, or at least a portion of a vehicle driver (not shown in FIG.1B).

Turning to FIG. 1C, the vehicle interior monitoring system 100 c mayinclude a driver-side A-pillar vehicle in-cabin device position 135 clocated in a driver side A-pillar 130 c, a passenger-side A-pillarvehicle in-cabin device position 145 c located in a passenger-sideA-pillar 140 c and a rearview mirror vehicle in-cabin device position160 c located on a bottom-side of the rearview mirror 155 c. The vehicleinterior monitoring system 100 c may further, or alternatively, includevehicle in-cabin device positions in a rearview mirror mounting bracket150 c and, or the steering wheel 110 c. While not shown in FIG. 1C, thevehicle monitoring system 100 c may include any other vehicle in-cabindevice positions as described in conjunction with FIGS. 1A and 1B. Asillustrated in FIG. 1C, data acquired from the vehicle in-cabin deviceposition 135 c, 145 c may be used to automatically determine a locationof the vehicle in-cabin device, a driver-side seat 190 c, apassenger-side seat 195 c, a steering wheel 110 c and, or at least aportion of a vehicle driver (not shown in FIG. 1C).

With reference to FIGS. 2A-2C, vehicle interiors 200 a, 200 b, 200 c aredepicted. As described in detail herein, data acquired from a vehiclein-cabin device 125 a, 135 a, 145 a, 160 a, 180 b of FIGS. 1A and 1B (orany other suitably located vehicle in-cabin device) may be used todetermine a position of at least a portion of a passenger 297 a withinthe vehicle interior 200 a. The data acquired from a vehicle in-cabindevice 125 a, 135 a, 145 a, 160 a, 180 b (or any other suitably locatedvehicle in-cabin device) may be used to determine whether, or not thepassenger 297 a is wearing a seatbelt 296 a. As further illustrated inFIG. 2A, data acquired from a vehicle in-cabin device 125 a, 135 a, 145a, 160 a, 180 b of FIGS. 1A and 1B (or any other suitably locatedvehicle in-cabin device) may be used to determine a position and, ororientation of a vehicle driver's head 219 a and, or right-hand 220 a ona steering wheel 210 a. For example, the data acquired from a vehiclein-cabin device 125 a, 135 a, 145 a, 160 a, 180 b may be used todetermine whether the vehicle driver's head 219 a is oriented toward arearview mirror 255 a, oriented toward the driver-side A-pillar 230 a ororiented toward the front windshield. The data acquired from the vehiclein-cabin device 125 a, 135 a, 145 a, 160 a, 180 b may be used todetermine whether the driver is wearing a seatbelt 291 a. In any event,the vehicle interior 200 a may include a vehicle in-cabin device havinga microphone 250 a located proximate the rearview mirror 255 a. Asdescribed in detail herein, data acquired from the microphone 250 a maybe used to determine a source of sound within the vehicle interior 200 aand, or a volume of the sound.

FIG. 2B depicts a vehicle interior 200 b including a driver-sideA-pillar vehicle in-cabin device position 235 b located on a driver-sideA-pillar 230 b. As described in detail herein, data acquired from thevehicle in-cabin device 235 b (along with any other suitably locatedvehicle in-cabin device) may be used to determine a position and, ororientation of a driver's head 219 b, the driver's left hand 215 b and,or right hand 220 b relative to the steering wheel 210 b. For example,data acquired from the vehicle in-cabin device 235 b (along with anyother suitably located vehicle in-cabin device) may be used to determinea gesture that the driver is performing with her left hand 215 b.

Turning to FIG. 2C, a vehicle interior 200 b depicts a vehicle in-cabindevice 260 c located on a bottom side of a rearview mirror 255 copposite a rearview mirror mount 250 c. As described in detail herein,data acquired from the vehicle in-cabin device 260 c (along with anyother suitably located vehicle in-cabin device) may be used to determinea position and, or orientation of a driver's head 219 c, the driver'sleft hand 215 c and, or right hand 220 c relative to the steering wheel210 c. For example, data acquired from the vehicle in-cabin device 260 c(along with any other suitably located vehicle in-cabin device) may beused to determine that the driver's head 219 c is oriented toward acellular telephone 221 c in her right hand 220 c. As also described indetail herein, a determination may be made that the driver isinattentive to the road based on the driver's head 219 c being orientedtoward the cellular telephone 221 c.

With reference to FIG. 3, a high-level block diagram of vehicle in-cabinsystem 300 is illustrated that may implement communications between avehicle in-cabin device 305 and a remote computing device 310 (e.g., aremote server) to provide vehicle in-cabin device 305 location and/ororientation data, and vehicle interior occupant position data to, forexample, an insurance related database 370. The vehicle in-cabin system300 may acquire data from a vehicle in-cabin device (e.g., positionsensors within a vehicle in-cabin device 125 a, 135 a, 145 a, 160 a, 180b of FIGS. 1A and 1B) and generate three dimensional (3D) models ofvehicle interiors and occupants as depicted in FIGS. 2A-2C. The vehiclein-cabin system 300 may also acquire data from a microphone (e.g.,microphone 250 a of FIG. 2A) and determine a source of sound and volumeof sound within a vehicle interior.

For clarity, only one vehicle in-cabin device 305 is depicted in FIG. 3.While FIG. 3 depicts only one vehicle in-cabin device 305, it should beunderstood that any number of vehicle in-cabin devices 305 may besupported. The vehicle in-cabin device 305 may include a memory 320 anda processor 325 for storing and executing, respectively, a module 321.The module 321, stored in the memory 320 as a set of computer-readableinstructions, may be related to a vehicle interior and occupant positiondata collecting application that, when executed on the processor 325,causes vehicle in-cabin device location data to be stored in the memory320. Execution of the module 321 may also cause the processor 325 togenerate at least one 3D model of at least a portion of a vehicleoccupant (e.g., a driver and/or passenger) within the vehicle interior.Execution of the module 321 may further cause the processor 325 toassociate the vehicle in-cabin device location data with a time and, ordate. Execution of the module 321 may further cause the processor 325 tocommunicate with the processor 355 of the remote computing device 310via the network interface 330, the vehicle in-cabin devicecommunications network connection 331 and the wireless communicationnetwork 315.

The vehicle in-cabin device 305 may also include a compass sensor 327, aglobal positioning system (GPS) sensor 329, and a battery 323. Thevehicle in-cabin device 305 may further include an image sensor input335 communicatively connected to, for example, a first image sensor 336and a second image sensor 337. While two image sensors 336, 337 aredepicted in FIG. 3, any number of image sensors may be included within avehicle interior monitoring system and may be located within a vehicleinterior as depicted in FIGS. 1A-1C. The vehicle in-cabin device 305 mayalso include an infrared sensor input 340 communicatively connected to afirst infrared sensor 341 and a second infrared sensor 342. While twoinfrared sensors 341, 342 are depicted in FIG. 3, any number of infraredsensors may be included within a vehicle interior monitoring system andmay be located within a vehicle interior as depicted in FIGS. 1A-1C. Thevehicle in-cabin device 305 may further include an ultrasonic sensorinput 345 communicatively connected to a first ultrasonic sensor 346 anda second ultrasonic sensor 347. While two ultrasonic sensors 346, 347are depicted in FIG. 3, any number of ultrasonic sensors may be includedwithin a vehicle interior monitoring system and may be located within avehicle interior as depicted in FIGS. 1A-1C. The vehicle in-cabin device305 may also include a microphone input 350 communicatively connected toa first microphone 351 and a second microphone 352. While twomicrophones 351, 352 are depicted in FIG. 3, any number of microphonesmay be included within a vehicle interior monitoring system and may belocated within a vehicle interior as depicted in FIGS. 1A-1C. Thevehicle in-cabin device 305 may further include a display/user inputdevice 325.

As one example, a first image sensor 336 may be located in a driver-sideA-pillar (e.g., location of position sensor 135 a of FIG. 1A), a secondimage sensor 337 may be located in a passenger-side A-pillar (e.g.,location of position sensor 145 a of FIG. 1A), a first infrared sensor341 may be located in a driver-side B-pillar (e.g., location of positionsensor 180 b of FIG. 1B), a second infrared sensor 342 may be located ina passenger-side B-pillar (not shown in the Figs.), first and secondultrasonic sensors 346, 347 may be located in a center portion of avehicle dash (e.g., location of position sensor 125 a of FIG. 1A) andfirst and second microphones 351, 352 may be located on a bottom portionof a vehicle interior rearview mirror (e.g., location of position sensor160 a of FIG. 1A). The processor 315 may acquire position data from anyone of, or all of, these sensors 336, 337, 341, 342, 346, 347, 351, 352and generate at least one 3D model (e.g., a 3D model of at least aportion of a vehicle driver) based on the position data. The processor315 may transmit data representative of at least one 3D model to theremote computing device 310. Alternatively, the processor 315 maytransmit the position data to the remote computing device 310 and theprocessor 355 may generate at least one 3D model based on the positiondata. In either event, the processor 315 or the processor 355 mayretrieve data representative of a 3D model of a vehicle operator, andmay compare at least a portion of the data representative of the 3Dmodel of vehicle driver with data representative of at least a portionof the 3D model vehicle operator. The processor 315 and, or theprocessor 355 may generate a vehicle driver warning based on thecomparison of the data representative of the 3D model of at least aportion of the vehicle driver with data representative of at least aportion of the 3D model vehicle operator to warn the vehicle operatorthat his position is indicative of inattentiveness. Alternatively, theprocessor 315 and/or the processor 355 may generate an advisory based onthe comparison of the data representative of the 3D model of at least aportion of the vehicle driver with data representative of at least aportion of the 3D model of a vehicle operator to advise the vehicleoperator how to correct her position to improve attentiveness.

The network interface 330 may be configured to facilitate communicationsbetween the vehicle in-cabin device 305 and the remote computing device310 via any hardwired or wireless communication network 315, includingfor example a wireless LAN, MAN or WAN, WiFi, the Internet, or anycombination thereof. Moreover, the vehicle in-cabin device 305 may becommunicatively connected to the remote computing device 310 via anysuitable communication system, such as via any publicly available orprivately owned communication network, including those that use wirelesscommunication structures, such as wireless communication networks,including for example, wireless LANs and WANs, satellite and cellulartelephone communication systems, etc. The vehicle in-cabin device 305may cause insurance risk related data to be stored in a remote computingdevice 310 memory 360 and/or a remote insurance related database 370.

The remote computing device 310 may include a memory 360 and a processor355 for storing and executing, respectively, a module 361. The module361, stored in the memory 360 as a set of computer-readableinstructions, facilitates applications related to determining a vehiclein-cabin device location and/or collecting insurance risk related data.The module 361 may also facilitate communications between the computingdevice 310 and the vehicle in-cabin device 305 via a network interface365, a remote computing device network connection 366 and the network315 and other functions and instructions.

The computing device 310 may be communicatively coupled to an insurancerelated database 370. While the insurance related database 370 is shownin FIG. 3 as being communicatively coupled to the remote computingdevice 310, it should be understood that the insurance related database370 may be located within separate remote servers (or any other suitablecomputing devices) communicatively coupled to the remote computingdevice 310. Optionally, portions of insurance related database 370 maybe associated with memory modules that are separate from one another,such as a memory 320 of the vehicle in-cabin device 305.

Turning to FIGS. 4, 5 and 6, a vehicle in-cabin device 405 of a vehiclein-cabin device data collection system 400 is depicted along withmethods 500, 600 of automatically determining a location and/ororientation of a vehicle in-cabin device 405 and, or transmittingrelated data to a remote server 310. The vehicle in-cabin device 405 maybe similar to the vehicle in-cabin device with insurance application 305of FIG. 3. The methods 500, 600 may be implemented by executing themodules 415-425 on a processor (e.g., processor 315).

In any event, the vehicle in-cabin device 405 may include a vehiclein-cabin device registration data receiving module 415, a vehiclein-cabin device reference location data receiving module 420, and avehicle in-cabin location determination module 425 stored in a memory420. The modules 415-425 may be stored in the memory 420 as a set ofcomputer-readable instructions that, when executed by the processor 315,may cause the processor 315 to automatically determine a vehiclein-cabin device 405 location and/or orientation relative to an interiorof a vehicle.

The vehicle in-cabin device 405 may be positioned within an interior ofa vehicle (block 505). The processor 315 may execute the vehiclein-cabin device registration data receiving module 415 to, for example,cause the processor 315 to receive vehicle in-cabin device registrationdata (block 510). The processor 315 may receive vehicle in-cabin deviceregistration data from, for example, at least one of a compass sensor327, a GPS sensor 329, an image sensor 336, 337, an infrared sensor 341,342, an ultrasonic sensor 346, 347, and/or a microphone 351, 352. Thevehicle in-cabin device registration data may be representative ofimages, and/or features (e.g., a vehicle occupant headlocation/orientation, a vehicle occupant hand location/orientation, avehicle occupant arm location/orientation, a vehicle occupant elbowlocation/orientation, a vehicle occupant torso location/orientation, aseat belt location, a cellular telephone location, a vehicle occupanteye location/orientation, a vehicle seat location/orientation, etc.)extracted from images, of an interior of a vehicle.

The processor 315 may execute the vehicle in-cabin device locationdetermination module 425 to, for example, cause the processor 315 toautomatically determine a vehicle in-cabin device 405 location within aninterior of a vehicle (block 515). For example, the processor 315 mayautomatically determine a vehicle in-cabin device 405 location and/ororientation based on the vehicle in-cabin device registration data. As aparticular example, the processor 315 may determine a vehicle in-cabindevice location and/or orientation based on data received from at leastone of: a global positioning system (GPS) sensor, an accelerometer, or agyroscope. Alternatively, or additionally, the processor 315 maydetermine an in-cabin device location and/or orientation by analyzingimage data for key landmarks, such as, for example, a car seat todetermine whether an image is right-side up, or upside down (e.g., if abottom of a car seat starts at a top portion of an image and a top ofthe car seat extends downward within the image, the processor 315 maydetermine the vehicle in-cabin device is upside down).

Based on the determined location and/or orientation, the processor 315,further executing the vehicle in-cabin device location determinationmodule 425, may adjust associated image such that real-time image datamay be correlated with previously classified image data. As a particularexample, a vehicle in-cabin device may be located and oriented such thatan associated sensor is 2 cm towards a driver from a passenger-sideA-pillar, 5 cm above a dashboard, and upside down (i.e., 180 degreerotation about the Z-axis). Thereby, the apparatuses, systems andmethods of the present disclosure may adjust current image dataright-side up and may compare the adjusted image data to previouslyclassified image data.

Turning to FIG. 6, a vehicle in-cabin device 405 may be positionedwithin an interior of a vehicle (block 605). The processor 315 mayexecute the vehicle in-cabin device registration data receiving module415 to, for example, cause the processor 315 to receive vehicle in-cabindevice registration data (block 610). The processor 315 may receivevehicle in-cabin device registration data from, for example, at leastone of a compass sensor 327, a GPS sensor 329, an image sensor 336, 337,an infrared sensor 341, 342, an ultrasonic sensor 346, 347, and/or amicrophone 351, 352. The vehicle in-cabin device registration data maybe representative of images, and/or features (e.g., a vehicle occupanthead location/orientation, a vehicle occupant hand location/orientation,a vehicle occupant arm location/orientation, a vehicle occupant elbowlocation/orientation, a vehicle occupant torso location/orientation, aseat belt location, a cellular telephone location, a vehicle occupanteye location/orientation, a vehicle seat location/orientation etc.)extracted from images, of an interior of a vehicle.

The processor 315 may execute the vehicle in-cabin device referencelocation data receiving module 420 to, for example, cause the processor315 to receive vehicle in-cabin device reference location data (block615). The vehicle in-cabin device reference location data may berepresentative of, for example, known locations and/or orientations ofvehicle in-cabin devices within a respective interior of a vehicle. Thevehicle in-cabin device reference location data may be representative ofimages, and/or features (e.g., a vehicle occupant headlocation/orientation, a vehicle occupant hand location/orientation, avehicle occupant arm location/orientation, a vehicle occupant elbowlocation/orientation, a vehicle occupant torso location/orientation, aseat belt location, a cellular telephone location, a vehicle occupanteye location/orientation, a vehicle seat location/orientation, etc.)extracted from images, of an interior of an associated vehicle. Theprocessor 315 may receive the vehicle in-cabin device reference locationdata from, for example, an insurance related database 370 via a remotecomputing device 310.

The processor 315 may execute the vehicle in-cabin locationdetermination module 425 to, for example, cause the processor 315 toautomatically determine a vehicle in-cabin device 405 location and/ororientation within an interior of a vehicle (block 620). For example,the processor 315 may automatically determine a vehicle in-cabin device405 location based on the vehicle in-cabin device registration data andthe vehicle in-cabin device reference location data. For example, theprocessor 315 may compare the vehicle in-cabin device registration data(e.g., images and/or features extracted from the images) with thevehicle in-cabin device reference location data (e.g., images and/orfeatures extracted from the images) to automatically determine a vehiclein-cabin device location and/or orientation.

A car-sharing insurance product could more specifically insure thedriver, regardless of the car. Traditional underwriting looks at thedriver-vehicle combination. For example, an insurance company weight therisk of the driver more heavily than any particular car. The methods andsystems of the present disclosure may allow car-sharing to get that riskinformation on the driver and carry it forward to whatever car they use.This would be tailored for that particular driver's behavior, ratherthan demographic and vehicle-use factors. This may allow certaincar-sharing entities to have a cost advantage. If the car-sharingentities are paying less, or more for specific insurance, thecar-sharing entities could pass those savings/costs to their customersand have a retention strategy.

The methods and systems of the present disclosure may allow foremergency responders by, for example, using gesture recognition systemsfrom an aftermarket/insurance device in order to provide an estimate tofirst responders about the severity of the crash and what kinds ofresources/equipment/expertise is required in order to extricate. Usingthe gesture recognition systems from an aftermarket/insurance device inorder to provide an estimate to first responders about the severity ofthe crash and what kinds of resources/equipment/expertise is required inorder to triage—have some idea of what emergency medical needs could beupon arrival. Since the “golden hour” is so critical, and it's notalways known how much of that hour has already expired, even apreliminary or broad clue could be helpful in the triage process. Theaftermarket gesture recognition device is already operating at the timeof the crash. It is collecting data about the driver's position/postureand the location of the arms relative to the body and structures in thevehicle (i.e. the steering wheel). Accelerometers in the device are ableto recognize that a crash has occurred (if a pre-determined accelerationthreshold has been reached). Upon crash detection the device couldtransmit via the driver's phone (which is already connected viaBluetooth) or perhaps transmit using an onboard transmitter that usesemergency frequencies (and therefore does not require consumer to payfor data fees). Using gesture recognition from any original equipment oraftermarket gesture tracking device, whether or not for insurancepurposes.

The methods and systems of the present disclosure may allow forTransition from Automated to Manual Driving Mode in the case of vehicleautomation systems operating the piloting functions with the human in asupervisory role. For example, the vehicle may encounter a situationwhere control needs to be transferred to the driver, but the driver mayor may not be ready to resume control. The methods and systems of thepresent disclosure may allow gesture recognition systems, or any gesturerecognition system, to be used to determine if the driver is ready toresume control. If he/she is not ready, then get his/her attentionquickly. The gesture recognition would be used to ascertain whether thedriver is ready to resume control by evaluating the driver's posture,the location of hands, the orientation of head, body language. Usemachine learning to evaluate driverengagement/attention/readiness-to-engage based on those variables. Thegesture recognition could be any original in-vehicle equipment oraftermarket device.

The methods and systems of the present disclosure may distinguishbetween Automated and Manual driving modalities for variable insurancerating for a scenario where there are many vehicles that are capable ofautomatically operating the piloting functions, and are capable of thedriver manually operating the piloting functions. The driver can electto switch between automated and manual driving modes at any point duringa drive. Gesture recognition would be utilized to distinguish whether adriver is operating the vehicle manually, or whether the vehicle isoperating automatically. This could be determined through either OEM oraftermarket hardware. The sensors and software algorithms are able todifferentiate between automatic and manual driving based on handmovements, head movements, body posture, eye movements. It candistinguish between the driver making hand contact with the steeringwheel (to show that he/she is supervising) while acting as a supervisor,versus the driver providing steering input for piloting purposes.Depending on who/what is operating the vehicle would determine whatreal-time insurance rates the customer is charged.

The methods and systems of the present disclosure may provide a tool formeasuring driver distraction where gesture recognition may be used toidentify, distinguish and quantify driver distracted for safetyevaluation of vehicle automation systems. This would be used to definemetrics and evaluate safety risk for the vehicle human-machine interfaceas a whole, or individual systems in the case where vehicles haveautomation and vehicle-to-vehicle/vehicle-to-infrastructurecommunication capabilities. With Vehicle automation: the vehicle iscapable of performing piloting functions without driver input. WithVehicle-to-vehicle/vehicle-to-infrastructure communication incorporated,the vehicle may be capable of communicating data about the first vehicledynamics or environmental traffic/weather conditions around the firstvehicle. For any entity looking to evaluate the safety or risk presentedby a vehicle with automated driving capabilities, gesture recognitioncould be useful to quantify risk presented by driver distractionresulting from any vehicle system in the cabin (i.e. an entertainmentsystem, a feature that automates one or more functions of piloting, aconvenience system). With the rise of vehicle automation systems andcapabilities, tools will be needed to evaluate the safety of individualsystems in the car, or the car as a whole. Much uncertainty remainsabout how these systems will be used by drivers (especially those whoare not from the community of automotive engineering or automotivesafety). Determining whether they create a net benefit to drivers is abig question. The methods and systems of the present disclosure mayallow gesture recognition to be used to identify the presence ofdistracted driving behaviors that are correlated with the presence ofvehicle automation capabilities. The distracted driver could bequantified by duration that the driver engages in certain behaviors.Risk quantification may also be measured by weighting certain behaviorswith higher severity than other behaviors, so the duration times areweighted. Risk quantification may also differentiate subcategories ofbehaviors based on degree of motion of hands, head, eyes, body. Forexample, the methods and systems of the present disclosure maydistinguish texting with the phone on the steering wheel from textingwith the phone in the driver's lap requiring frequent glances up anddown. The latter would be quantified with greater risk in terms ofseverity of distraction. The purpose of this risk evaluation could befor reasons including but not limited to adhering to vehicleregulations, providing information to the general public, vehicle designtesting or insurance purposes.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One may be implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

What is claimed is:
 1. A vehicle in-cabin device for generating datarepresentative of at least one skeletal diagram of at least one occupantwithin an associated vehicle, the vehicle in-cabin device comprising: aprocessor and a memory; at least one sensor for generating vehicleinterior data, wherein the vehicle interior data is representative of alocation of the vehicle in-cabin device within the vehicle; a featureextracting module stored on the memory that, when executed by theprocessor, causes the processor to extract features from the vehicleinterior data, wherein the features are representative of objects withinthe vehicle; a vehicle in-cabin device location determination modulestored on the memory that, when executed by the processor, causes theprocessor to generate vehicle in-cabin device location data based on thefeatures extracted from the vehicle interior data, wherein the vehiclein-cabin device location data is representative of a location andorientation of the vehicle in-cabin device within the vehicle interior;and an adjustment algorithm stored on the memory that, when executed bythe processor, causes the processor to adjust features that areextracted from the vehicle interior data based on the vehicle in-cabindevice location data such that the features are correlated withpreviously classified features.
 2. The vehicle in-cabin device as inclaim 1, wherein the at least one sensor is selected from: at least onedigital image sensor, at least one ultra-sonic sensor, at least oneradar-sensor, at least one infrared light sensor, or at least one laserlight sensor.
 3. The vehicle in-cabin vehicle device as in claim 1,wherein the vehicle interior data is representative of athree-dimensional location of the vehicle in-cabin device within thevehicle interior.
 4. The vehicle in-cabin vehicle device as in claim 1,wherein the vehicle interior data is representative of an orientation ofthe vehicle in-cabin device selected from: a vehicle in-cabin devicetilt value, a vehicle in-cabin device pan value, or a vehicle in-cabindevice roll value.
 5. The vehicle in-cabin device as in claim 1, whereinthe vehicle interior data is representative of a three-dimensionallocation of the vehicle in-cabin device within the vehicle interior andan orientation of the vehicle in-cabin device selected from: a vehiclein-cabin device tilt value, a vehicle in-cabin device pan value, or avehicle in-cabin device roll value.
 6. The vehicle in-cabin device as inclaim 1, further comprising: a compass for generating compass data,wherein the compass data is representative of an orientation of thevehicle in-cabin device within the vehicle interior.
 7. The vehiclein-cabin device as in claim 1, wherein the at least one sensor includesa first digital image sensor and a second digital image sensor, and thevehicle interior data is representative of a three-dimensional locationof the vehicle in-cabin device within the vehicle interior.
 8. Acomputer-implemented method for automatically registering a location ofa vehicle in-cabin device within an interior of a vehicle, the methodcomprising: positioning the vehicle in-cabin device within the interiorof the vehicle; receiving, at a processor of a computing device, vehicleinterior data from a sensor of the vehicle in-cabin device; extractingfeatures from the vehicle interior data, wherein the features arerepresentative of objects within the vehicle; receiving, at theprocessor of the computing device, vehicle in-cabin device registrationlocation data that is based on the features that are extracted from thevehicle interior data, wherein the vehicle in-cabin device registrationlocation data is representative of at least one of: a two-dimensionallocation of the vehicle in-cabin device within the vehicle or athree-dimensional location of the vehicle in-cabin device within thevehicle and at least one of: a vehicle in-cabin device tilt value, avehicle in-cabin device pan value, or a vehicle in-cabin device rollvalue; and adjusting features that are extracted from the vehicleinterior data based on the vehicle in-cabin device location data suchthat the features are correlated with previously classified features. 9.The method of claim 8, further comprising: receiving, at the processorof the computing device, vehicle in-cabin device reference data, whereinthe vehicle in-cabin device reference data is representative of knownvehicle in-cabin device locations and orientations.
 10. The method ofclaim 9, further comprising: comparing, using the processor of thecomputing device, the vehicle in-cabin device registration location dataand the vehicle in-cabin device reference data.
 11. The method of claim8, further comprising: receiving, at a processor of the computingdevice, vehicle in-cabin device reference image data, wherein thevehicle in-cabin device reference data is representative of knownvehicle in-cabin device locations and orientations.
 12. The method ofclaim 8, further comprising: extracting features from vehicle in-cabindevice reference data, wherein the features are representative of itemswithin the vehicle.
 13. The method of claim 12, wherein the vehiclein-cabin device registration location data is based on the features. 14.A non-transitory computer-readable medium storing computer-readableinstruction that, when executed by a processor, cause the processor toautomatically register a location of a vehicle in-cabin device within aninterior of a vehicle, the non-transitory computer-readable mediumcomprising: a feature extracting module that, when executed by theprocessor, causes the processor to extract features from vehicleinterior data that is received from a sensor of the vehicle in-cabindevice, wherein the features are representative of objects within thevehicle; a vehicle in-cabin device registration location data receivingmodule that, when executed by the processor, causes the processor toreceive vehicle in-cabin device registration location data based on thefeatures that are extracted from the vehicle interior data, wherein thevehicle in-cabin device registration location data is representative ofat least one of: a two-dimensional location of the vehicle in-cabindevice within the vehicle or a three-dimensional location of the vehiclein-cabin device within the vehicle and at least one of: a vehiclein-cabin device tilt value, a vehicle in-cabin device pan value, or avehicle in-cabin device roll value; and an adjustment algorithm that,when executed by the processor, causes the processor to adjust featuresthat are extracted from the vehicle interior data based on the vehiclein-cabin device location data such that the features are correlated withpreviously classified features.
 15. The non-transitory computer-readablemedium of claim 14, further comprising: a vehicle in-cabin devicereference data receiving module that, when executed by a processor,causes the processor to receive vehicle in-cabin device reference data,wherein the vehicle in-cabin device reference data is representative ofknown vehicle in-cabin device locations and orientations.
 16. Thenon-transitory computer-readable medium of claim 15, further comprising:a comparing module that, when executed by a processor, causes theprocessor to compare the vehicle in-cabin device registration locationdata and the vehicle in-cabin device reference data.
 17. Thenon-transitory computer-readable medium of claim 14, further comprising:a vehicle in-cabin device reference image data receiving module that,when executed by the processor, causes the processor to receive vehiclein-cabin device reference image data, wherein the vehicle in-cabindevice reference data is representative of known vehicle in-cabin devicelocations and orientations.
 18. The non-transitory computer-readablemedium of claim 14, further comprising: a feature extracting modulethat, when executed by the processor, causes the processor to extractfeatures from vehicle in-cabin device reference data, wherein thefeatures are representative of items within the vehicle.
 19. Thenon-transitory computer-readable medium of claim 18, wherein the vehiclein-cabin device registration location data is based on the features. 20.The non-transitory computer-readable medium of claim 19, wherein thevehicle in-cabin device reference image data is representative of knownfeatures.